Johanna Petersen

Pervasive Computing Technologies to Continuously Assess Alzheimer’s Disease Progression and Intervention Efficacy

Traditionally, assessment of functional and cognitive status of individuals with dementia occurs in brief clinic visits during which time clinicians extract a snapshot of recent changes in individuals’ health. Conventionally, this is done using various clinical assessment tools applied at the point of care and relies on patients’ and caregivers’ ability to accurately recall daily activity and trends in personal health. These practices suffer from the infrequency and generally short durations of visits. Since 2004, researchers at the Oregon Center for Aging and Technology (ORCATECH) at the Oregon Health and Science University have been working on developing technologies to transform this model. ORCATECH researchers have developed a system of continuous in-home monitoring using pervasive computing technologies that make it possible to more accurately track activities and behaviors and measure relevant intra-individual changes. We have installed a system of strategically placed sensors in over 480 homes and have been collecting data for up to 8 years. Using this continuous in-home monitoring system, ORCATECH researchers have collected data on multiple behaviors such as gait and mobility, sleep and activity patterns, medication adherence, and computer use. Patterns of intra-individual variation detected in each of these areas are used to predict outcomes such as low mood, loneliness, and cognitive function. These methods have the potential to improve the quality of patient health data and in turn patient care especially related to cognitive decline. Furthermore, the continuous real-world nature of the data may improve the efficiency and ecological validity of clinical intervention studies.

Unobtrusive In-Home Detection of Time Spent Out-of-Home With Applications to Loneliness and Physical Activity

Loneliness is a common condition in elderly associated with severe health consequences including increased mortality, decreased cognitive function, and poor quality of life. Identifying and assisting lonely individuals is therefore increasingly important-especially in the home setting-as the very nature of loneliness often makes it difficult to detect by traditional methods. One critical component in assessing loneliness unobtrusively is to measure time spent out-of-home, as loneliness often presents with decreased physical activity, decreased motor functioning, and a decline in activities of daily living, all of which may cause decrease in the amount of time spent outside the home. Using passive and unobtrusive in-home sensing technologies, we have developed a methodology for detecting time spent out-of-home based on logistic regression. Our approach was both sensitive (0.939) and specific (0.975) in detecting time out-of-home across over 41 000 epochs of data collected from four subjects monitored for at least 30 days each in their own homes. In addition to linking time spent out-of-home to loneliness, (r = -0.44, p = 0.011) as measured by the UCLA Loneliness Index, we demonstrate its usefulness in other applications such as uncovering general behavioral patterns of elderly and exploring the link between time spent out-of-home and physical activity ( r = 0.415, p = 0.031), as measured by the Berkman Social Disengagement Index.

Time Out-of-Home and Cognitive, Physical, and Emotional Wellbeing of Older Adults: A Longitudinal Mixed Effects Model.

Background Time out-of-home has been linked with numerous health outcomes, including cognitive decline, poor physical ability and low emotional state. Comprehensive characterization of this important health metric would potentially enable objective monitoring of key health outcomes. The objective of this study is to determine the relationship between time out-of-home and cognitive status, physical ability and emotional state. Methods and Findings Participants included 85 independent older adults, age 65–96 years (M = 86.36; SD = 6.79) who lived alone, from the Intelligent Systems for Assessing Aging Changes (ISAAC) and the ORCATECH Life Laboratory cohorts. Factors hypothesized to affect time out-of-home were assessed on three different temporal levels: yearly (cognitive status, loneliness, clinical walking speed), weekly (pain and mood) or daily (time out-of-home, in-home walking speed, weather, and season). Subject characteristics including age, race, and gender were assessed at baseline. Total daily time out-of-home in hours was assessed objectively and unobtrusively for up to one year using an in-home activity sensor platform. A longitudinal tobit mixed effects regression model was used to relate daily time out-of-home to cognitive status, physical ability and emotional state. More hours spend outside the home was associated with better cognitive function as assessed using the Clinical Dementia Rating (CDR) Scale, where higher scores indicate lower cognitive function (β CDR = -1.69, p<0.001). More hours outside the home was also associated with superior physical ability (β Pain = -0.123, p<0.001) and improved emotional state (β Lonely = -0.046, p<0.001; β Low mood = -0.520, p<0.001). Weather, season, and weekday also affected the daily time out-of-home. Conclusions These results suggest that objective longitudinal monitoring of time out-of-home may enable unobtrusive assessment of cognitive, physical and emotional state. In addition, these results indicate that the factors affecting out-of-home behavior are complex, with factors such as living environment, weather and season significantly affecting time out-of-home. Studies investigating the relationship between time out-of-home and health outcomes may be optimized by taking into account the environment and life factors presented here.

Associations between observed in-home behaviors and self-reported low mood in community-dwelling older adults.

To ascertain the association between self‐report of low mood and unobtrusively measured behaviors (walking speed, time out of residence, frequency of room transitions, and computer use) in community‐dwelling older adults using novel monitoring technologies.

Actigraphy-Based Scratch Detection Using Logistic Regression

Incessant scratching as a result of diseases such as atopic dermatitis causes skin break down, poor sleep quality, and reduced quality of life for affected individuals. In order to develop more effective therapies, there is a need for objective measures to detect scratching. Wrist actigraphy, which detects wrist movements over time using microaccelerometers, has shown great promise in detecting scratch because it is lightweight, usable in the home environment, can record longitudinally, and does not require any wires. However, current actigraphy-based scratch-detection methods are limited in their ability to discriminate scratch from other nighttime activities. Our previous work demonstrated the separability of scratch from both walking and restless sleep using a clustering technique which employed four features derived from the actigraphic data: number of accelerations above 0.01 gs, epoch variance, peak frequency, and autocorrelation value at one lag. In this paper, we extended these results by employing these same features as independent variables in a logistic regression model. This allows us to directly estimate the conditional probability of scratching for each epoch. Our approach outperforms competing actigraphy-based approaches and has both high sensitivity (0.96) and specificity (0.92) for identifying scratch as validated on experimental data collected from 12 healthy subjects. The model must still be fully validated on clinical data, but shows promise for applications to clinical trials and longitudinal studies of scratch.

Phone behaviour and its relationship to loneliness in older adults

Objectives: Loneliness and social isolation are two important health outcomes among older adults. Current assessment of these outcomes relies on self-report which is susceptible to bias. This paper reports on the relationship between loneliness and objective measures of isolation using a phone monitoring device. Method: Phone monitors were installed in the homes of 26 independent elderly individuals from the ORCATECH Life Laboratory cohort (age 86 ± 4.5, 88% female) and used to monitor the daily phone usage for an average of 174 days. Loneliness was assessed using the 20-item University of California Los Angeles (UCLA) Loneliness scale. A mixed effects negative binomial regression was used to model the relationship between loneliness and social isolation, as assessed using the total number of calls, controlling for cognitive function, pain, age, gender, and weekday. A secondary analysis examined the differential effect of loneliness on incoming and outgoing calls. Results: The average UCLA Loneliness score was 35.3 ± 7.6, and the median daily number of calls was 4. Loneliness was negatively associated with telephone use (IRR = 0.99, p < 0.05). Daily phone use was also associated with gender (IRR = 2.03, p < 0.001) and cognitive status (IRR = 1.51, p < 0.001). The secondary analysis revealed that loneliness was significantly related to incoming (IRR = 0.98, p < 0.01) but not outgoing calls. Conclusions: These results demonstrate the close relationship between loneliness and social isolation, showing that phone behaviour is associated with emotional state and cognitive function. Because phone behaviour can be monitored unobtrusively, it may be possible to sense loneliness levels in older adults using objective assessments of key aspects of behaviour.

A state-space model for finger tapping with applications to cognitive inference

Sensory-motor functions have been repeatedly linked to both cognitive and physical functions. One common test of sensory-motor performance frequently used for neuropsychological evaluation is the Halstead-Reitan finger tapping test (FTT). While this test has been normed and used extensively, the underlying sensory, motor and cognitive processes mediating tapping behavior during the test are not well understood. As a first step towards investigating the behavioral aspects manifested by these processes, we describe a state-space model for finger tapping during the FTT. This state-space model exploits quasiperiodicity to decompose tapping into a set of time-varying states corresponding to the instantaneous amplitude of the finger oscillation, the instantaneous frequency (or speed) of tapping, and a phase that keeps track of the current finger position during the cycle. We evaluate the model by showing a good fit between estimated and actual measurements, and outline an experiment that will relate features from the model to cognitive function.

Longitudinal Relationship Between Loneliness and Social Isolation in Older Adults Results From the Cardiovascular Health Study

Objective: To understand the longitudinal relationship between loneliness and isolation. Method: Participants included 5,870 adults 65 years and older (M = 72.89 ± 5.59 years) from the first 5 years of the Cardiovascular Health Study. Loneliness was assessed using a dichotomized loneliness question. Social isolation was assessed using six items from the Lubben Social Network Scale. Yearly life events were included to assess abrupt social network changes. Mixed effects logistic regression was employed to analyze the relationship between isolation and loneliness. Results: Higher levels of social isolation were associated with higher odds of loneliness, as was an increase (from median) in level of social isolation. Life events such as a friend dying were also associated with increased odds of loneliness. Discussion: These results suggest that average level of isolation and increases in the level of isolation are closely tied to loneliness, which has implications for future assessment or monitoring of loneliness in older adult populations.